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علی زاده, پریسا, کامکار, بهنام, شتایی, شعبان, کاظمی, حسین. (1397). برآوردتغییرات سطح زیرکشت گندم و سویا با استفاده از طبقه‌بندی تصاویر ماهواره‌ای در غرب استان گلستان. سامانه مدیریت نشریات علمی, 31(3), 41-61. doi: 10.22092/aj.2018.121231.1268
پریسا علی زاده; بهنام کامکار; شعبان شتایی; حسین کاظمی. "برآوردتغییرات سطح زیرکشت گندم و سویا با استفاده از طبقه‌بندی تصاویر ماهواره‌ای در غرب استان گلستان". سامانه مدیریت نشریات علمی, 31, 3, 1397, 41-61. doi: 10.22092/aj.2018.121231.1268
علی زاده, پریسا, کامکار, بهنام, شتایی, شعبان, کاظمی, حسین. (1397). 'برآوردتغییرات سطح زیرکشت گندم و سویا با استفاده از طبقه‌بندی تصاویر ماهواره‌ای در غرب استان گلستان', سامانه مدیریت نشریات علمی, 31(3), pp. 41-61. doi: 10.22092/aj.2018.121231.1268
علی زاده, پریسا, کامکار, بهنام, شتایی, شعبان, کاظمی, حسین. برآوردتغییرات سطح زیرکشت گندم و سویا با استفاده از طبقه‌بندی تصاویر ماهواره‌ای در غرب استان گلستان. سامانه مدیریت نشریات علمی, 1397; 31(3): 41-61. doi: 10.22092/aj.2018.121231.1268

برآوردتغییرات سطح زیرکشت گندم و سویا با استفاده از طبقه‌بندی تصاویر ماهواره‌ای در غرب استان گلستان

پژوهش های کاربردی زراعی
مقاله 3، دوره 31، شماره 3 - شماره پیاپی 120، مهر 1397، صفحه 41-61    اصل مقاله (1.35 M)
نوع مقاله: مقاله پژوهشی
شناسه دیجیتال (DOI): 10.22092/aj.2018.121231.1268
نویسندگان
پریسا علی زاده1؛ بهنام کامکار* 2؛ شعبان شتایی2؛ حسین کاظمی2
1گروه زراعت، دانشکده تولید گیاهی، دانشگاه علوم کشاورزی و منابع طبیعی گرگان
2دانشگاه علوم کشاورزی و منابع طبیعی گرگان
چکیده
برآورد سطح زیر کشت بر اساس شاخصهای جنبی از جمله میزان بذر، کود و سم تحویلشده به کشاورزان و همچنین برآوردهای شرکتهای مشاور کنترل میگردد که کاری زمان‌بر و پر‌هزینه می‌باشد. بنابراین این مطالعه با هدف برآورد سطح زیر کشت دو محصول گندم و سویا در طی سالهای 2000 تا 2016 با استفاده از تصاویر ماهواره لندست انجام شد. بعد از پیش پردازش و پردازش های لازم، و تهیه نمونه های تعلیمی مناسب از مزارع کشت سویا و گندم، طبقه بندی تصاویر با استفاده از دو روش طبقهبندی شبکه عصبی مصنوعی و ماشین ‏بردار پشتیبان انجام شد و جهت طبقهبندی با هدف تفکیک این دو محصول زراعی دادههای واقعیت زمینی، نقشه NDVI اراضی زراعی و شناسایی رفتار طیفی نقاط آموزشی گندم و سویا بکار برده شدند. نقشه های حاصل از طبقه بندی با استفاده از نقاط واقعیت زمینی مورد ارزیابی صحت قرار گرفتند. با توجه به نتایج بهدستآمده از بررسی ضـریب کاپـا و صـحت کلـی، روش ماشین‏بردار پشتیبان برای طبقه‌بندی اراضی کشاورزی و تفکیک محصولات نسبت به روش شبکه عصبی موفق‌تر بود و در همه تصاویر مقدار صحت کلی محاسبهشده و ضریب کاپا به ترتیب بیشتر از 80٪ و بیش از 8/0میباشد که این نشان‌دهنده قابلاعتماد بودن نتایج طبقهبندیاست. طبق نتایج، حدود 93 درصد از نقاط برآورد‌شده سطح زیرکشت گندم و سویا در طی 16 سال مورد مطالعه در محدوده حدود اطمینان 15± درصد قرار دارند که نشان می‏دهد این روش، روش مطمئنی برای تفکیک این دو محصول با استفاده از تصاویر اردیبهشت‏ماه (برای گندم) و شهریورماه (برای سویا) می‏باشد.
کلیدواژه‌ها
سنجش از دور؛ طبقه‌بندی؛ شبکه عصبی مصنوعی؛ ماشین‏بردار پشتیبان
عنوان مقاله [English]
Estimation of changes in land area under wheat and soybean cultivation using satellite images classification techniques in west of Golestan province
نویسندگان [English]
Parisa Alizadeh1؛ Behnam Kamkar2؛ shaban shataee2؛ hossein kazemi posht masari2
1Department of Agriculture, Faculty of Plant Production, Gorgan University of Agricultural Sciences and Natural Resources
2Agronomy Dept., Gorgan University of Agricultural Sciences and Natural Resources
چکیده [English]
Introduction:Estimation of cultivated area is controlled on the basis of some features such as seed rate, fertilizer and other delivered chemical inputs which are costly andtime consuming. In this way, using satellite data as a new solution not only reduces the drawbacks of conventional estimation methods of cultivated area , but also can be helpful for various programming aims in the agriculture scope. Land cover mapping is important for much planning and management activities. Today, satellite images and remote sensing techniques are extensively used in all sectors, including agriculture and natural resources because they provide updated data and high analyzing abilities. It is one of the fastest and most cost-effective methods to map those lands are available for researchers. In recent years, sattelite imagery and different detection methods have helped researches to detect the features at a lower cost and spend less time. Different methods are avialbale for this purpose.. Each method has some advantages and disadvantages. Artificial neural network, fuzzy logic, support vector machine, decision tree, object-oriented classification and intelligent systems can be considered as advanced classification methods (Guo et al., 2012). In this study, we want to detect wheat and soybean-grown fields with two advanced classification methods (i.e. support vector machine (SVM) and artificial neural network (ANN)) in west of Golestan Province, Iran. Cultivation areas were determined from those maps extracted from satellite images to use them as the base layer for other research goals.
Materials and Methods:The current research was conducted with the aim of estimating the wheat- and soybean-grown areas during a 16-year period from 2000 to 2016 using Landsat satellite imagery. To this purpose, two classification methods, support vector machine and artificial neural network, were used. In order to classify and detect aforementioned two crops, ground control points (GCPs), the normalized difference vegetation index (NDVI) for agricultural lands, and spectral behavior of wheat and soybean training GCPs were involved. In order to validate the results of classification, the generated maps were checked by GCPs (coordinated with GPS).
Results and Discussion: Wheat and soybeans were at the maximum vegetative growth in May and September, and were well-detected from other crops. So the images of these two months were used for detection in all studied years. According to the results of previous studies, support vector machine and artificial neural network could be used as two reliable image classification methods for detecting the crops vegetation cover (Rahimzadegan & Pourgholam ,2017; Mokhtari & Najafi, 2015). The accuracy of image classification was assessed using kappa coefficient and overall accuracy. Kappa coefficient and overall accuracy showed the support vector machine method was superior than artificial neural network method in classifying agricultural lands and detecting studied wheat- and soybean-grown fields. In the all images, the calculated overall accuracy coefficient was more than 80% (0.84 to 0.92 for wheat; and 0.84 to 0.90 for soybean) and Kappa coefficient was more than 0.8, indicating the reliability of the classification outputs. According to comparisons of satellite image-based estimations and real recorded ststistics, about 93% of the estimated areas of wheat - and soybean grown fields in the 16 consecutive years of study were within the range of 15% confidence level, which indicates that this method is a reliable method for detecting these two crops using images of April (for wheat) and September (for soybean).
Conclusions: The support vector machine method of classification was identified as the superior method. wheat- and soybean-grown fields maps extracted from satellite images can be used as a base layer for regional modeling, providing yield gap layer, calculating the water requirement, designing crop pattern and etc.
کلیدواژه‌ها [English]
Remote Sensing, Classification, Artificial Neural Networks, Support Vector Machine
مراجع

Amini Bazyani, S., Akbari, M., and Zare Abyane, H. 2013. Assessment of Cropped Area, and Cropping Intensity in Hamedan–Bahar Plain Using Remote Sensing Data. Iranian Journal of lrrigation and Drainage, 7 (1), 36-48 (In Persian with English Summary).
Brian, W. S., Qi, C., and Michael, B. 2011. A comparison of classification techniques to support land cover and land use analysis in tropical coastal zones. Applied Geography, 31: 525-532.
Chuanga, W.C., Lina, C.Y., Chiena, C.H., and Choub, W.C. 2011. Application of Markov-chain model for vegetation restoration assessment at landslide areas caused by a catastrophic earthquake in Central Taiwan. Ecological Modelling, 222 (2011), 835- 845.
Chubey, M.S., Franklin, S.E., Wulder, M.A. 2006. Object-based analysis of Ikonos-2 imagery for extraction of forest inventory parameters. Photogrammetric Engineering & Remote Sensing, 72(4), 383-394.
Fahimnejad, H., Sofbof, S., Ali Mohammadi, A., and Valadan Zagh, M. 2008. Detection of agricultural crop using Hyperion Data. In: Geomatics Conference, Tehran, Iran, 11-14 March 2008, p. 91-96 (In Persian).
Foody, M. G., and Mathur, A. 2006. The use of small training sets containing mixed pixels for accurate hard classification: Training on mixed spectral response for classification by SVM. Remote Sensing of Environment, 103 (2), 179-189.
Golestan agricultural and natural resources research and education center. 2017. Available at Web site http://www.ganrrc.org.ir (accessed 5 September 2017).
Guo, Y., De Jong, K., Liu, F., Wang, X., and Li, C. 2012. A Comparison of Artificial Neural Networks and Support Vector Machines on Land Cover Classification, Springer-verlag Berlin Heidelberg. CCIS, 316: 531 -539.
Huang, C. L., Davis, S., and Townshend, J.R.G. 2002. An assessment of support vector machines for land cover classification, International Journal of Remote Sensing, 23(4), 725-749.
Jiang, X., Lin, M., and Zhao, J. 2011. Woodland cover change assessment using decision trees, support vector machines and artificial neural networks classification algorithms. In: Fourth Workshop on Intelligent Computation Technology and Automation, China, 20-23 April 2011, p. 312-315.
Kavzoglu, T., and Colkesen, I. 2009. A kernel functions analysis for support vector machines for land cover classification, International Journal of Applied Earth Observation and Geoformation, 11: 352-359.
Khajehedin, S.J., and Pourmanafi, P. 2008. Determination of paddy cultivate area of Zayandehrud brink with the data of IRS11 satellite sensors in Isfahan, Journal of Sciences and Technology of Agriculture and Natural Resources, 1: 513-528 (In Persian).
Lu, D., and Q. Weng. 2007. A survey of image classification methods and techniques for improving classification performance. Remote Sensing, 28 (5), 823-87.
Marsden, T., Murdoch, J., and Morgan, K. 1999. Sustainable agriculture, food supply chains and regional development: editorial introduction. International Planning Studies, 4 (3), 301-295.
Mather, P.M., and Tso, B. 2009. Classification Methods for Remotely Sensed Data. CRC Press, New York.
Ministry of Agriculture Jihad. 2016. Agricultural Statistics First Volume - Crop Products. Center for Statistics and Information (In Persian).
Mokhtari, M.D., and Najafi, a. 2015. Comparison of support vector machine and artificial neural network classification methods for land user utilization from Landsat TM satellite imagery. Journal of Sciences and Technology of Agriculture and Natural Resources, 19 (72), 35-44 (In Persian).
Petropoulos, G., Kontoes, P., and Keramitsoglou, C. 2011. Burnt area delineation from a unitemporal perspective based on Landsat TM imagery classification using support vector machines. International Journal of Applied Earth Observation and Geoinformation, 13(1), 70-80.
Pôças, I., Cunha, M., Pereira, L.S., and Allen, R.G. 2013. Using remote sensing energy balance and evapotranspiration to characterize montane landscape vegetation with focus on grass and pasture lands. International Journal of Applied Earth Observation and Geoinformation, 21: 159–172.
Qasemlou, N., Mobashari, M., and Rezaei, Y. 2009. Detection of Plant Type Using Spectral Properties and Artificial Neural Networks. In: Geomatics Conference, Tehran, Iran, 15-18 February 2009, p. 10-15 (In Persian).
Rahimzadegan, M., and Pourgholam. M. 2017. Identification of the area under cultivation of Saffron using Landsat-8 temporal satellite images (Case study: Torbat Heydarieh). RS & GIS for Natural Resources, 7 (4), 97-115 (In Persian).
Richards J., A. 2013. Remote sensing digital image analysis. springer.
Rngzan, K., Esfandiari, A., and Taghizadeh, A. 2011. Detection and determination of sugarcane cultivated area on Hyperion Images. In: Geomatics Conference, Tehran, Iran, 15-18 February 2011, p. 40-45 (In Persian).
Safiyanian, A., Mohammadi Tofigh, A., khodakarami, L., and Amiri. F. 2011. Preparation of land use map using Artificial Neural Network method (Case Study: Kabudarahang Basin, Razan and Khonjin - Talkhab, Hamedan Province). Journal of Applied RS and GIS Techniques in Natural, 2 (1), 1-12 (In Persian).
Samadzadegan, F., and Hassani, H. 2012. Determination of Optimal Support Vector Machines in the Classification of hyperspectral Images Based on Genetic Algorithm. Journal of Information and Communication Technology, 13: 9-24 (In Persian).
Sanaiei Nejad, H., Sadr Abadi Haghighi, R., Klarostaqi, Kiumars., and Shah Tahmasebi, A. 2008. Estimation of wheat cultivated area using TERRA satellite imagery in Khorasan Razavi province. Agricultural science and technology, 21(2), 67-74 (In Persian).
Xu, M., Watanachaturaporn, P., Varshney, P.K., and Arora, M.K. 2005. Decision tree regression for soft classification of remote sensing data. Remote Sensing of Environment, 97(3), 322- 336.
Yang, C.C., Prasher, S.O., Enright, P., Madramootoo, C., Burgess, M., Goel, P.K, and Callum, I. 2003. Application of decision tree technology for image classification using remote sensing data . Agricultural Systems 76(3), 1101-1117.
Zadehdifard, N. 2002. Preparation of land use map using satellite data in Baft drainage basin. MSc Disseration, Faculty of Agriculture, Isfahan University of Technology, Iran (In Persian with English Summary).
Ziaeian-Firoozabadi, P., Sayad-Bydhndy, L., and Eskandari-Nodeh, M. 2009. Mapping and estimating the area under rice cultivation in Sari city using satellite images Radarst. Geography Research Natural, 68: 45-58 (In Persian).

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